Learning Probabilistic Relational Planning Rules

Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR2004)

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Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR2004)

Contents

Tolearn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains.